INF 550 Section 3.7
Natasha Wesely
2022-09-27
3.7 USA-NPN Coding Lab
#1
For the purposes of this exercise we will be focusing on two NEON sites: HARV and CPER. Save these two sites into your workplace so that you can feed them into functions and packages.
sitesOfInterest <- c("HARV", "CPER")#2
Define AGGD and write the equation using LaTeX. What is an appropriate time interval over which we should calculate AGGD?
AGGD is the Accumulated Growing Degree Day, which uses the “accumulated” temperature in an ecosystem to predict phenological change.
\(GDD = ((T_{max} + T_{min})/2) - T_{base}\)
An appropriate time interval over which we should calculate AGGD could be the growing season, which varies based on location.
#3
Use the neonUtilities package to pull plant phenology observations (DP1.10055.001). We will work with the statusintensity data
#TOS Phenology Data
dpid <- as.character('DP1.10055.001') #phe data
pheDat <- loadByProduct(dpID="DP1.10055.001",
site = sitesOfInterest,
package = "basic",
check.size = FALSE,
token=NEON_TOKEN)## Finding available files
##
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##
## Downloading files totaling approximately 93.742811 MB
## Downloading 165 files
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##
## Unpacking zip files using 1 cores.
## Stacking operation across a single core.
## Stacking table phe_perindividual
## Stacking table phe_statusintensity
## Stacking table phe_perindividualperyear
## Copied the most recent publication of validation file to /stackedFiles
## Copied the most recent publication of categoricalCodes file to /stackedFiles
## Copied the most recent publication of variable definition file to /stackedFiles
## Finished: Stacked 3 data tables and 4 metadata tables!
## Stacking took 3.154484 secs#NEON sends the data as a nested list, so I need to undo that
# unlist all data frames
list2env(pheDat ,.GlobalEnv)## <environment: R_GlobalEnv>summary(phe_perindividualperyear)## uid namedLocation domainID siteID
## Length:1623 Length:1623 Length:1623 Length:1623
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## plotID date editedDate
## Length:1623 Min. :2013-09-05 00:00:00 Min. :2013-08-20 00:00:00
## Class :character 1st Qu.:2015-09-14 00:00:00 1st Qu.:2015-05-26 00:00:00
## Mode :character Median :2018-07-11 00:00:00 Median :2018-03-15 00:00:00
## Mean :2018-02-07 03:32:03 Mean :2017-09-24 09:43:19
## 3rd Qu.:2020-07-23 00:00:00 3rd Qu.:2020-06-30 00:00:00
## Max. :2022-06-20 00:00:00 Max. :2022-05-24 00:00:00
## NA's :11
## individualID patchOrIndividual canopyPosition plantStatus
## Length:1623 Length:1623 Length:1623 Length:1623
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## stemDiameter measurementHeight maxCanopyDiameter ninetyCanopyDiameter
## Min. : 0.00 Min. : 10 Min. : 0.000 Min. : 0.000
## 1st Qu.: 11.93 1st Qu.:130 1st Qu.: 0.200 1st Qu.: 0.125
## Median : 23.45 Median :130 Median : 0.400 Median : 0.400
## Mean : 26.65 Mean :117 Mean : 4.188 Mean : 3.243
## 3rd Qu.: 40.88 3rd Qu.:130 3rd Qu.: 7.700 3rd Qu.: 5.600
## Max. :100.00 Max. :150 Max. :21.700 Max. :20.000
## NA's :1001 NA's :1001 NA's :393 NA's :393
## patchSize percentCover height diseaseType
## Min. :0.0630 Min. : 0.10 Min. : 0.000 Length:1623
## 1st Qu.:0.0630 1st Qu.: 9.00 1st Qu.: 0.100 Class :character
## Median :0.0630 Median :18.00 Median : 0.300 Mode :character
## Mean :0.1433 Mean :24.38 Mean : 6.615
## 3rd Qu.:0.2500 3rd Qu.:33.00 3rd Qu.:13.825
## Max. :0.2500 Max. :99.00 Max. :56.000
## NA's :1481 NA's :1306 NA's :47
## samplingProtocolVersion measuredBy recordedBy
## Length:1623 Length:1623 Length:1623
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## remarks dataQF publicationDate release
## Length:1623 Length:1623 Length:1623 Length:1623
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## summary(phe_statusintensity)## uid namedLocation domainID siteID
## Length:289666 Length:289666 Length:289666 Length:289666
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## plotID date editedDate
## Length:289666 Min. :2013-08-23 00:00:00 Min. :2015-03-19 00:00:00
## Class :character 1st Qu.:2016-03-23 00:00:00 1st Qu.:2016-05-09 00:00:00
## Mode :character Median :2018-04-23 00:00:00 Median :2018-04-23 00:00:00
## Mean :2018-04-12 16:55:33 Mean :2018-05-31 18:52:43
## 3rd Qu.:2020-09-03 00:00:00 3rd Qu.:2020-09-03 00:00:00
## Max. :2022-08-11 00:00:00 Max. :2022-08-15 00:00:00
## NA's :450
## dayOfYear individualID phenophaseName phenophaseStatus
## Min. : 2 Length:289666 Length:289666 Length:289666
## 1st Qu.:121 Class :character Class :character Class :character
## Median :178 Mode :character Mode :character Mode :character
## Mean :185
## 3rd Qu.:251
## Max. :364
## NA's :3861
## phenophaseIntensityDefinition phenophaseIntensity samplingProtocolVersion
## Length:289666 Length:289666 Length:289666
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## measuredBy recordedBy remarks dataEntryRecordID
## Length:289666 Length:289666 Length:289666 Length:289666
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## dataQF publicationDate release
## Length:289666 Length:289666 Length:289666
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## #remove duplicate records
phe_statusintensity <- select(phe_statusintensity, -uid)
phe_statusintensity <- distinct(phe_statusintensity)
#Format dates
phe_statusintensity$date <- as.Date(phe_statusintensity$date, "%Y-%m-%d")## Warning in as.POSIXlt.POSIXct(x, tz = tz): unknown timezone '%Y-%m-%d'phe_statusintensity$editedDate <- as.Date(phe_statusintensity$editedDate, "%Y-%m-%d")## Warning in as.POSIXlt.POSIXct(x, tz = tz): unknown timezone '%Y-%m-%d'phe_statusintensity$year <- as.numeric(substr(phe_statusintensity$date, 1, 4))
phe_statusintensity$month <- as.numeric(format(phe_statusintensity$date, format="%m"))
df = phe_statusintensity %>%
left_join(phe_perindividual, by = "individualID") %>%
filter(phenophaseName == "Colored leaves",
taxonID == "QURU",
phenophaseStatus == "yes") %>%
select(date.x, year, month, dayOfYear, siteID.x, individualID, phenophaseIntensity) %>%
na.omit()Yes, there are ways to extract numerical values for string data that could be used for plotting. For example, you could count how many observations there are for each string type and make some kind of density visual. You could also subset the string to grab the numberical values and then convert those to numerical objects and use them for plotting directly.
#4
Using dpid DP1.00002.001 Single Aspirated Air Temperature calculate AGGD based on NEON tower data over the time period you decided upon in question 1. To save you time and frustration I’ve placed some mostly complete example code for one height on the tower just for Harvard. You will need to determine which height you think it best and complete these calculations for both sites. You will also need to consider things like filtering your temperature data for quality flags, and converting from GMT (Greenwich Mean Time) to your location’s time:
dpid <- as.character('DP1.00002.001') ##single aspirated air temperature
tempDat <- loadByProduct(dpID=dpid,
site = sitesOfInterest,
startdate = "2017-01",
enddate="2017-12",
avg=30,
package = "basic",
check.size = FALSE)## Input parameter avg is deprecated; use timeIndex to download by time interval.
## Finding available files
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##
## Downloading files totaling approximately 12.78088 MB
## Downloading 100 files
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##
## Stacking operation across a single core.
## Stacking table SAAT_30min
## Merged the most recent publication of sensor position files for each site and saved to /stackedFiles
## Copied the most recent publication of variable definition file to /stackedFiles
## Finished: Stacked 1 data tables and 3 metadata tables!
## Stacking took 1.00864 secsSAAT <- tempDat$SAAT_30min
# GDD typically reported in F
# convert df temps
SAAT$meanTempF=SAAT$tempSingleMean*1.8+32
SAAT$endDateTime = with_tz(SAAT$endDateTime, tzone = "America/New_York")
#pull date value from dateTime
SAAT$date <- substr(SAAT$endDateTime, 1, 10)
select(tempDat$sensor_positions_00002, c(HOR.VER, zOffset))## HOR.VER zOffset
## 1: 000.010 0.19
## 2: 000.020 5.29
## 3: 000.030 16.26
## 4: 000.040 22.52
## 5: 000.050 29.60
## 6: 000.010 0.16
## 7: 000.020 1.81
## 8: 000.030 3.87head(tempDat$sensor_positions_00002)## siteID HOR.VER name
## 1: HARV 000.010 CFGLOC100471
## 2: HARV 000.020 CFGLOC100474
## 3: HARV 000.030 CFGLOC100477
## 4: HARV 000.040 CFGLOC100480
## 5: HARV 000.050 CFGLOC100483
## 6: CPER 000.010 CFGLOC100238
## description start end
## 1: Harvard Forest Single Aspirated Air Temperature L1 2010-01-01T00:00:00Z NA
## 2: Harvard Forest Single Aspirated Air Temperature L2 2010-01-01T00:00:00Z NA
## 3: Harvard Forest Single Aspirated Air Temperature L3 2010-01-01T00:00:00Z NA
## 4: Harvard Forest Single Aspirated Air Temperature L4 2010-01-01T00:00:00Z NA
## 5: Harvard Forest Single Aspirated Air Temperature L5 2010-01-01T00:00:00Z NA
## 6: Central Plains Single Aspirated Air Temperature L1 2010-01-01T00:00:00Z NA
## referenceName referenceDescription referenceStart referenceEnd xOffset
## 1: TOWER100450 Harvard Forest Tower 2010-01-01T00:00:00Z NA 5.36
## 2: TOWER100450 Harvard Forest Tower 2010-01-01T00:00:00Z NA 5.35
## 3: TOWER100450 Harvard Forest Tower 2010-01-01T00:00:00Z NA 5.35
## 4: TOWER100450 Harvard Forest Tower 2010-01-01T00:00:00Z NA 5.35
## 5: TOWER100450 Harvard Forest Tower 2010-01-01T00:00:00Z NA 5.35
## 6: TOWER100223 Central Plains Tower 2010-01-01T00:00:00Z NA 5.36
## yOffset zOffset pitch roll azimuth referenceLatitude referenceLongitude
## 1: 2.40 0.19 0 0 0 42.53691 -72.17265
## 2: 2.36 5.29 0 0 0 42.53691 -72.17265
## 3: 2.36 16.26 0 0 0 42.53691 -72.17265
## 4: 2.36 22.52 0 0 0 42.53691 -72.17265
## 5: 2.36 29.60 0 0 0 42.53691 -72.17265
## 6: 2.40 0.16 0 0 0 40.81554 -104.74559
## referenceElevation eastOffset northOffset xAzimuth yAzimuth publicationDate
## 1: 348.13 -5.36 -2.40 270 180 20211211T013906Z
## 2: 348.13 -5.35 -2.36 270 180 20211211T013906Z
## 3: 348.13 -5.35 -2.36 270 180 20211211T013906Z
## 4: 348.13 -5.35 -2.36 270 180 20211211T013906Z
## 5: 348.13 -5.35 -2.36 270 180 20211211T013906Z
## 6: 1653.92 -5.36 -2.40 270 180 20211210T202950Zday_temp <- SAAT%>%
filter(verticalPosition=="030",
finalQF == 0)%>%
group_by(siteID, date)%>%
mutate(dayMaxTemp=max(meanTempF), dayMinTemp=min(meanTempF),
dayMeanTemp=mean(meanTempF))%>%
select(siteID, date, dayMaxTemp, dayMinTemp, dayMeanTemp)%>%
distinct()
##alternative, simplified mean, consistent with many GDD calculations
### does accumulation differ for true mean vs. simplified mean?
day_temp$mean2 <- (day_temp$dayMinTemp + day_temp$dayMaxTemp)/2
day_temp$GDD1 <- ifelse(day_temp$dayMeanTemp-50 < 0, 0, round(day_temp$dayMeanTemp-50, 0))
day_temp$GDD2 <- ifelse(day_temp$mean2-50 < 0, 0, round(day_temp$mean2-50, 0))
day_temp$GDD3 <- ifelse(day_temp$dayMeanTemp-50 < 0, 0, round(day_temp$mean2-50, 0))
# define year
day_temp$year <- substr(day_temp$date, 1, 4)
#function to add daily GDD values
sumr.2 <- function(x) {
sapply(1:length(x), function(i) sum(x[1:i]))
}
#calculate Accumlated GDD
day_temp$AGDD3 <- sumr.2(x=day_temp$GDD3)
day_temp$AGDD2 <- sumr.2(x=day_temp$GDD2)
day_temp$AGDD1 <- sumr.2(x=day_temp$GDD1)
day_temp <- ungroup(day_temp)
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutHARV.df = day_temp %>%
filter(siteID == "HARV") %>%
select(date, AGDD1, AGDD2, AGDD3)
CPER.df = day_temp %>%
filter(siteID == "CPER") %>%
select(date, AGDD1, AGDD2, AGDD3)
p1 = plot_ly() %>%
add_trace(
x= ~HARV.df$date,
y = ~ HARV.df$AGDD1,
type= 'scatter',
mode = "lines",
line = list(width = 1, color = "rgb(120,120,120)"),
name = "Calculated Mean Temp",
showlegend = TRUE,
opacity=.5
)%>%
add_trace(
data = HARV.df,
x = ~ date,
y = ~ AGDD2,
name= 'Simplified Mean Temp',
showlegend = TRUE,
type = 'scatter',
mode = 'lines',
line = list(width = 1),
opacity=.5)%>%
add_trace(
data = HARV.df,
x = ~ date,
y = ~ AGDD3,
name= 'Filtered Using Both',
showlegend = TRUE,
type = 'scatter',
mode = 'lines',
line = list(width = 1),
opacity=.2)
p1p2 = plot_ly() %>%
add_trace(
x= ~CPER.df$date,
y = ~ CPER.df$AGDD1,
type= 'scatter',
mode = "lines",
line = list(width = 1, color = "rgb(120,120,120)"),
name = "Calculated Mean Temp",
showlegend = TRUE,
opacity=.5
)%>%
add_trace(
data = CPER.df,
x = ~ date,
y = ~ AGDD2,
name= 'Simplified Mean Temp',
showlegend = TRUE,
type = 'scatter',
mode = 'lines',
line = list(width = 1),
opacity=.5)%>%
add_trace(
data = CPER.df,
x = ~ date,
y = ~ AGDD3,
name= 'Filtered Using Both',
showlegend = TRUE,
type = 'scatter',
mode = 'lines',
line = list(width = 1),
opacity=.2)
p2#5
Plot your calculated AGGD and comment on your calculations. Do you need to revise your time horizon or sensor height?
After doing the calculations and looking at my plots the first time, I went back and changed my sensor height. I realized after plotting that the sensor height I had picked was not available at the CPRE site. Because the taxon I picked is an oak, I wanted to use the highest sensor. But I had to picked the highest sensor height that was present at both sites.
#6
Now we’re going to build a model to see how AGGD impacts phenological status. But Wait. Is phenology all driven by temperature? Should you consider any other variables? What about AGGD and just plain temperature? Also, we have one very temperate site, and another that is a semi-arid grassland. Should water availability of any sort be considered? Any other variables or data?
Yes, it has been widely documented that phenological change is driven by more than just growing degree days. It’s well researched that temperature, solar radiation (photo period), and water availability all strongly impact phenology in addition to AGGD.
Create a GAM (Generalized Additive Model) for your phenological data including any variables you think might be relevant.
# set up the data
day_temp = day_temp %>%
mutate(
date = ymd(date)
)
gam.df = df %>%
mutate(
phenoInstNumb = case_when(
phenophaseIntensity == "< 5%" ~ 5,
phenophaseIntensity == "5-24%" ~ 15,
phenophaseIntensity == "25-49%" ~ 37,
phenophaseIntensity == "50-74%" ~ 62,
phenophaseIntensity == "75-94%" ~ 85,
phenophaseIntensity == ">= 95%" ~ 95,
)
) %>%
rename(date = date.x, siteID = siteID.x) %>%
left_join(day_temp, by = c("date", 'siteID')) %>%
filter(siteID == "HARV") %>%
# get rid of any dates outside of 2017
filter(year(date) == 2017)
library(mgcv)
model <- mgcv::gam(phenoInstNumb ~ AGDD3 + s(dayMeanTemp) + s(dayOfYear),
data = gam.df)
mgcv::summary.gam(model) ##
## Family: gaussian
## Link function: identity
##
## Formula:
## phenoInstNumb ~ AGDD3 + s(dayMeanTemp) + s(dayOfYear)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -23.13175 59.15861 -0.391 0.696
## AGDD3 0.02038 0.03741 0.545 0.586
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(dayMeanTemp) 8.357 8.823 6.439 <2e-16 ***
## s(dayOfYear) 8.592 8.882 17.006 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.407 Deviance explained = 42.5%
## GCV = 93.383 Scale est. = 90.338 n = 581mgcv::plot.gam(model, pages=1 )
I tried a several different GAMs with a variety variables and decided this was the best model.
# 7-8
7. Now that we have a model for NEON data, let’s use the rnpn package to see how adding additional data could improve our fit. Use the taxonID that you selected at each NEON tower, and feed that to the rnpn package to grab observational data and increase your number of observations.
8. Pull AGGD from USA-NPN based on the observations you just pulled.
npn.df = npn_download_status_data(
request_source = 'NAU',
years = c('2017'),
states = c("MA"),
agdd_layer = 50,
# get only observations for Quercus rubra
species_ids = 102
)## using a custom handler function.## opening curl input connection.##
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##
Found 5000 records...## closing curl input connection.## Service is currently unavailable. Please try again later!npn.df = npn.df %>%
filter(phenophase_description == "Colored leaves",
intensity_value != -9999) %>%
select(day_of_year, observation_date,
update_datetime, intensity_value,
genus, species, site_id, `gdd:agdd_50f`) %>%
mutate(
phenoInstNumb = case_when(
intensity_value == "Less than 5%" ~ 5,
intensity_value == "5-24%" ~ 15,
intensity_value == "25-49%" ~ 37,
intensity_value == "50-74%" ~ 62,
intensity_value == "75-94%" ~ 85,
intensity_value == "95% or more" ~ 95,
)
) %>%
rename(date = observation_date,
dayOfYear = day_of_year,
AGDD3 = `gdd:agdd_50f`)#9
Combine your NEON and USA-NPN data into the same data.frame and re-fit your GAM.
Summarize your new model
Plot your new model
# add the NPN data to the GAM dataframe
subgam1 = gam.df %>%
# grab only the vars we need
select(date, phenoInstNumb, AGDD3, dayOfYear)
subgam2 = npn.df %>%
select(date, phenoInstNumb, AGDD3, dayOfYear)
subgam3 = rbind(subgam1, subgam2)
newgam.df = left_join(subgam3, day_temp, by = "date")
model <- mgcv::gam(phenoInstNumb ~ AGDD3.x + s(dayMeanTemp) + s(dayOfYear),
data = newgam.df)
mgcv::summary.gam(model) ##
## Family: gaussian
## Link function: identity
##
## Formula:
## phenoInstNumb ~ AGDD3.x + s(dayMeanTemp) + s(dayOfYear)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -13.941570 2.406611 -5.793 8.65e-09 ***
## AGDD3.x 0.015109 0.001353 11.165 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(dayMeanTemp) 8.607 8.950 3.787 0.000168 ***
## s(dayOfYear) 8.831 8.988 49.434 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.509 Deviance explained = 51.6%
## GCV = 177.09 Scale est. = 174.5 n = 1328mgcv::plot.gam(model, pages=1 )
Comment on your new model: was it improved? If so how?
Yes, my model did improve some. My R2 has increased and my residuals are smaller.